Thermal-inertial solutions have recently received considerable attention for odometry systems in challenging scenarios. The technology was not widely used due to its cost and poor resolution. However, recent advances in thermal imaging have made thermal cameras more affordable, with a smaller footprint, higher quality and modest power consumption enabling the portability to robotics applications [1]}.
The work by Delaune et al. [2]}, and Khattak et al. [3]}, have shown the use of raw thermal data to navigate in dark scenarios where conventional visual cameras fail. Despite the outstanding results obtained by these works, thermal-infrared cameras suffer from photometric inconsistencies due to the thermography generation process. As a result, a standard computer vision program that relies on the photometric consistency assumption might fail to deliver reliable perception data in a sequence of frames. In this direction, the work by Das et al. [4]}, develops a photometric model of Thermal Infrared (TIR) cameras to attenuate such disturbances, obtaining photometric consistency over time and space. However, place recognition with thermal imaging remains an open challenge, and its application in collaborative systems has not been investigated.
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